Multi-object tracking (MOT) has drawn great attention in recent years. This technique is critically needed in many tasks, such as traffic flow analysis [1]}, [2]}, [3]}, [4]}, human behavior prediction and pose estimation [5]}, [6]}, [7]}, [8]}, [9]}, autonomous driving assistance [10]}, [11]}, [12]} and even for underwater animal abundance estimation [13]}, [14]}, [15]}. Recent years have seen the emergence of a variety of tracking algorithms, from graph clustering methods [16]}, [17]}, [1]}, [19]}, [20]} to graph neural networks [21]}, [22]}, [23]}, [24]} that aggregate information across frames and objects, from tracking-by-detection paradigm to joint detection and tracking [25]}, [26]}, [21]}, [28]}, [29]}, [30]} to improve the detection performance with multiple frames, from Kalman filtering [31]} to recurrent neural networks (RNN) [32]} and long-short term memory (LSTM) [33]} to boost association performance with the motion clue. However, due to the noisy visual object detection and occlusion, tracking multiple objects over a long time is yet very challenging.
